An introduction to reinforcement learning for neuroscience (2311.07315v3)

Abstract: Reinforcement learning (RL) has a rich history in neuroscience, from early work on dopamine as a reward prediction error signal (Schultz et al., 1997) to recent work proposing that the brain could implement a form of 'distributional reinforcement learning' popularized in machine learning (Dabney et al., 2020). There has been a close link between theoretical advances in reinforcement learning and neuroscience experiments throughout this literature, and the theories describing the experimental data have therefore become increasingly complex. Here, we provide an introduction and mathematical background to many of the methods that have been used in systems neroscience. We start with an overview of the RL problem and classical temporal difference algorithms, followed by a discussion of 'model-free', 'model-based', and intermediate RL algorithms. We then introduce deep reinforcement learning and discuss how this framework has led to new insights in neuroscience. This includes a particular focus on meta-reinforcement learning (Wang et al., 2018) and distributional RL (Dabney et al., 2020). Finally, we discuss potential shortcomings of the RL formalism for neuroscience and highlight open questions in the field. Code that implements the methods discussed and generates the figures is also provided.

• The paper reviews and integrates classical and modern RL methods to elucidate how neural circuits adapt and generate behavior.
• It demonstrates the biological relevance of algorithms like temporal difference learning, Q-learning, deep RL, and distributional approaches in modeling neural activity.
• The study highlights hybrid methods such as successor representations and DYNA architectures, suggesting future directions for biologically plausible RL models.

An Introduction to Reinforcement Learning for Neuroscience

The intersection of reinforcement learning (RL) and neuroscience has provided a unique perspective on how biological systems learn from their environment, adapting over time through experiences. This paper by Kristopher T. Jensen offers an in-depth review of classical and modern reinforcement learning methods and their applicability in neuroscientific research, outlining both foundational algorithms and recent advancements. It explores how these methods have been instrumental in modeling neural processes and behaviors.

Overview of Reinforcement Learning and Neuroscience

Reinforcement learning models the process by which an agent learns to make sequences of decisions by optimizing cumulative rewards. This framework has deep roots in neuroscience, particularly in the paper of how neural circuits encode value and guide decision-making. The paper meticulously traces the history from early demonstrations of dopamine as a reward prediction error signal to contemporary theories suggesting its role in distributional reinforcement learning. Throughout, it underscores the tight coupling between theoretical advancements in RL and empirical findings in neuroscience.

Classical Approaches and Their Biological Correlates

The paper begins by presenting a foundational overview of classical RL algorithms, such as temporal difference (TD) learning and Q-learning, which have both informed and been informed by neuroscientific experiments. The canonical TD learning algorithm, for instance, has been foundational in developing theories concerning the role of dopamine in the brain's reward system. This investigation is extended through a discussion of model-free and model-based RL paradigms, emphasizing their relevance to animal behavior and neural activity patterns.

Incorporating Modern Developments: Deep and Distributional RL

The discussion progresses to deep reinforcement learning, which encompasses the usage of non-linear function approximation through neural networks. This has allowed for solving complex problems with high-dimensional input spaces, analogous to real-world scenarios faced by biological organisms. The implications of meta-RL, where learning from across tasks and environments is encoded in network dynamics, are explored with reference to prefrontal cortex functions.

Furthermore, a fascinating domain explored is distributional RL, where Jensen highlights research suggesting that VTA dopamine neurons encode not just scalar prediction errors but entire distributions of potential outcomes. This aligns RL theory with observed neural phenomena, offering insights into how animals might represent uncertainty and variability in reward scenarios.

The Role of Successor Representations and DYNA

The paper thoroughly addresses hybrid RL approaches such as successor representation, which bridges the gap between model-free and model-based methods. Successor representations provide a middle ground that allows for efficient adaptation to changing reward structures without exhaustive planning, thus offering a plausible explanation for observed neural dynamics and behavioral flexibility.

The DYNA architecture is another focal point, where the paper articulates its relevance to understanding biological replay mechanisms, such as those observed in hippocampal circuits, proposing that these replays might underlie prioritized memory and planning in natural environments.

Connections to Neural and Cognitive Processes

Each section considers the biological parallels of these RL methods, indicating specific neural substrates that might implement such algorithms. For example, the involvement of different striatal regions in learning and decision-making strategies or the role of frontal cortex in implementing meta-RL as adaptive strategies over tasks.

Key Implications and Future Directions

The paper concludes with forward-looking speculation on the integration of RL models in neuroscience. The exploration of hierarchical reinforcement learning, multi-agent interactions, and the development of more biologically plausible RL algorithms are suggested avenues. The potential symbiosis between self-supervised representation learning and RL in driving neural dynamics is particularly highlighted.

Overall, Kristopher T. Jensen's paper serves as a detailed bridge between computational theories of learning and their neural implementations, offering a rich resource for those seeking to understand how RL principles apply to neuroscience and vice versa. This synergy is pivotal for advancing both fields in addressing complex learning and decision-making processes inherent in natural systems.